I need reasons why to choose one method Any suggestions are recommended

Thanks

-------------Problems Reply------------

What do you want to learn? What should be the output? Is the input just the used action? If you are learning a model of the environment, it is expressed by a probability distribution:

P(next_state|state, action)

It is common to use a separate model for each action. That makes the mapping between input and output simpler. The input is a vector of state features. The output is a vector of the features of the next state. The used action is implied by the model.

The state features could be encoded as bits. An active bit would indicate the presence of a feature.

This would learn a deterministic model. I don't know what is a good way to learn a stochastic model of the next states. One possibility may be to use stochastic neurons.

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